This year I purchased my first mouse with customisable keys - Logi M650, and I was mind-blown. I've always had a bad time remembering keyboard shortcuts and this mouse was a real life saver for me - as I could customise all my keyboard shortcuts on my mouse. it's the best gadget I purchased this year. What was your best purchase of 2022 that improved your productivity?
I was scrolling through X today when one of my X friends shared screenshots of two employees who recently moved from Anthropic to OpenAI.
We had an era when people were leaving OpenAI for Anthropic in large numbers, Sam Altman was becoming the second most-hated Mark Zuckerberg, and Anthropic was attracting talent from OpenAI.
I've used ChatGPT and other AI tools while building my startup, and they've made it possible to take on work that would normally require a much larger team. Research, technical problem-solving, planning, and exploring new ideas can happen much faster. But one thing I've learned is that completing more work doesn't necessarily mean the business is moving forward at the same speed. You can build faster, solve technical problems faster, and make improvements faster, while the harder questions about customers, adoption, and business growth still take time to answer. It's changed how I think about productivity as a founder. I'm learning to distinguish between how much work gets completed and whether that work is actually moving the business forward.
For founders building with AI, what's one lesson you've learned about the difference between moving faster and making real business progress?
Last month, we finally got a demo with a massive prospect a 500-person company that was exactly our target audience. The demo went perfectly. The VP of Product loved our tool, and the team was ready to sign a $1,500/month contract.
Then, the deal moved to their IT and Security department.
I've spent a lot of time thinking about the difference between testing a product and validating it.
When you're building software, there are so many things you can measure. Does the functionality work? Are the calculations correct? Can users complete the workflow? Are the security controls working as intended?
Those are important questions, and you can spend months getting them right.
But there's another question that technical testing can't fully answer:
As the title suggests, I want to discuss strategies that SaaS companies have implemented or hope to implement on converting their free users into a free trial, and hopefully a paid plan. What have your most successful campaigns been vs some of your worst to get these conversions? Hearing the details of both successful & non-successful campaigns could hopefully streamline the process for other startups and help increase paid users!
Lately, I ve been looking closely at how independent builders and small teams are managing AI knowledge bases. It feels like the default "industry standard" is to immediately reach for a complex RAG pipeline and a heavy, paid Vector Database.
But I'm starting to wonder if we are over-engineering this for 90% of standard use cases.
Vector DBs are incredibly powerful for massive scale, but for smaller or non-massive datasets, they can be expensive, complex to query, and act as complete black boxes. If a search returns a weird chunk, diagnosing it is often a nightmare.
99% of custom GPTs are of poor quality, but I believe there's still that 1% that are incredibly valuable. Have you ever encountered a GPT that you just couldn't stop using once you started?
Stress is an all-too-common experience that can impact anyone at any time. Here are a few ideas that I usually practice to slow down my thoughts when stress hits: Deep breathing exercises Short meditation Brief workout A walk to the shop to buy ice cream Your turn. Do you have a special technique to combat stress?
